Cloud bills rarely spike because one person made one mistake. They drift upward because ownership is unclear, resources sprawl across teams and accounts, and guardrails (tagging, budgets, policies, cleanup automation) aren’t enforced. The result: idle environments running 24/7, overprovisioned instances, “small” storage and egress charges compounding monthly, and commitments that don’t match real usage.
This guide is a practical cloud cost optimization checklist: 25 places teams waste money, grouped into the five biggest waste categories. You’ll get detection signals, concrete fixes, typical savings impact (labeled as typical), and a 30-day sprint plan to capture quick wins without breaking reliability. If you want, you can also download a Cloud Cost Optimization Scorecard + Tracker to operationalize this into weekly savings and governance.
Quick Answer Box
- What a cloud cost optimization checklist is: A structured list of high-probability cost leaks with detection signals, owners, and fix actions—so you can reduce cloud costs without guesswork.
- The 5 biggest waste categories: idle/overprovisioned compute, storage + data transfer surprises, managed services misconfiguration, commitment/pricing mistakes, and governance/process gaps.
- Typical savings range (typical): 10–30% for organizations without mature FinOps; 5–15% for orgs with basic hygiene already. Drivers include workload variability, tag coverage, rightsizing, and commitment utilization.
- How to use this checklist in 30 days: Week 1 visibility + quick kills → Week 2 rightsizing/scheduling/cleanup → Week 3 commitments + network fixes → Week 4 governance + automation + KPI cadence.
Who This Checklist Is For
This checklist is for:
- Startups scaling fast: bills grow faster than revenue; environments multiply.
- Mid-market SaaS: multi-service sprawl, Kubernetes, and dev/test waste.
- Enterprise multi-account: chargeback gaps, decentralized ownership, and duplicated tooling.
- Data-heavy orgs: storage, logs, egress, and managed analytics costs explode quietly.
It won’t help much if:
- You already run mature FinOps with continuous governance, automation, and high tag coverage; or
- Your cloud bill is mostly fixed commitments with very high utilization (you may still find savings, but they’ll come from architecture/workload changes rather than hygiene).
The 5 Categories of Cloud Waste
A) Idle & overprovisioned compute
Why it happens: teams provision for peak, forget to turn off non-prod, and don’t own ongoing rightsizing.
B) Storage & data transfer surprises
Why it happens: “cheap per GB” becomes expensive at scale; retention is unmanaged; egress and cross-AZ traffic are invisible until billing.
C) Managed services misconfiguration
Why it happens: defaults are expensive; autoscaling and throughput settings aren’t tuned; features get enabled “just in case.”
D) Commitment & pricing strategy mistakes
Why it happens: reserved instances/savings plans/commitments are bought without a usage model; utilization and coverage aren’t tracked.
E) Governance, tagging, and process gaps
Why it happens: nobody owns cost outcomes; budgets and alerts aren’t enforced; tagging isn’t required; showback/chargeback is missing.
The Checklist: 25 Places Teams Waste Money
Use the sections below as your FinOps checklist and assign an owner per item. Each item includes detection + fix actions.
A) Idle & Overprovisioned Compute (1–7)
1) Unused/idle instances (“zombie” compute)
- What it is: instances running with near-zero CPU/network/disk activity.
- Why it happens: dev/test left on; abandoned projects; no shutdown policy.
- How to detect: low average CPU (e.g., <5–10% typical), low network, no deployments; look for instances with no recent log activity.
- Fix:
- Identify candidates and owners
- Stop/terminate or schedule off-hours
- Enforce TTL tags (auto-expire non-prod)
- Typical savings impact (typical): high; often immediate “found money.”
- Owner: Cloud/DevOps + App team
2) Overprovisioned instance sizes (rightsizing not done)
- What it is: paying for bigger machines than workload needs.
- Why it happens: fear of downtime; set-and-forget; no measurement.
- How to detect: sustained low CPU and memory; low I/O; compare to instance class capacity.
- Fix:
- Rightsize down 1–2 steps in non-prod first
- Use autoscaling where appropriate
- Add performance monitoring and rollback plan
- Typical savings impact (typical): medium to high depending on baseline.
- Owner: DevOps/Platform + App team
3) Non-prod running 24/7 (dev/stage/QA)
- What it is: environments left on outside business hours.
- Why it happens: no schedules; “someone might need it.”
- How to detect: environment tags + uptime; cost by environment.
- Fix:
- Implement schedules (e.g., 8am–8pm weekdays)
- One-click “wake” workflow
- Exception list for critical systems
- Typical savings impact (typical): high for dev-heavy orgs.
- Owner: DevOps + Engineering managers
4) Always-on GPU/ML instances (or specialized compute)
- What it is: expensive compute left running between jobs.
- Why it happens: manual workflows; jobs not queued; no auto-stop.
- How to detect: GPU utilization low; long idle periods; job scheduler logs.
- Fix:
- Auto-stop when idle
- Move to job-based execution (batch/spot where safe)
- Right-size GPU class to workload
- Typical savings impact (typical): very high if this exists.
- Owner: ML/AI team + Platform
5) Orphaned load balancers with low/no traffic
- What it is: LBs that remain after services are deprecated.
- Why it happens: teardown not in the process; fear of breaking routing.
- How to detect: near-zero requests; no target group health activity.
- Fix:
- Confirm owners and dependencies
- Remove unused LBs and DNS records
- Add infrastructure-as-code cleanup checks
- Typical savings impact (typical): low to medium, but common.
- Owner: DevOps/Platform
6) Underutilized autoscaling groups (min too high)
- What it is: ASGs configured with high minimum capacity even off-peak.
- Why it happens: conservative defaults; no scaling policies tuning.
- How to detect: instance count steady; utilization low.
- Fix:
- Reduce min capacity safely
- Tune scaling metrics and cooldown
- Validate with load tests
- Typical savings impact (typical): medium.
- Owner: Platform + App team
7) Lack of rightsizing automation
- What it is: rightsizing is manual and sporadic.
- Why it happens: no tooling, no ownership cadence.
- How to detect: repeated overprovisioning findings; no monthly rightsizing report.
- Fix:
- Implement monthly rightsizing reviews
- Add policy-based recommendations and approvals
- Automate non-prod rightsizing where safe
- Typical savings impact (typical): medium; improves continuously.
- Owner: FinOps + Platform
B) Storage & Data Transfer Surprises (8–13)
8) Orphaned volumes and snapshots
- What it is: unattached disks and snapshots accumulating over time.
- Why it happens: terminated instances leave storage behind; backup defaults.
- How to detect: unattached volumes; snapshot age and growth trends.
- Fix:
- Identify unattached volumes and owners
- Delete or archive old snapshots
- Set retention policies and automation
- Typical savings impact (typical): low to medium; adds up.
- Owner: DevOps/Platform
9) Storage class misalignment (hot data stored as “premium” forever)
- What it is: expensive storage tier used for rarely accessed data.
- Why it happens: lifecycle policies not defined; fear of retrieval time.
- How to detect: access frequency reports; object age distribution.
- Fix:
- Set lifecycle rules to move cold data to cheaper tiers
- Archive data with clear retrieval process
- Review “must-be-hot” assumptions
- Typical savings impact (typical): medium.
- Owner: Data team + Platform
10) Log retention runaway
- What it is: logs retained at high volume and high retention by default.
- Why it happens: “keep everything” mentality; no compliance-driven policy.
- How to detect: log storage growth; ingestion cost spikes; retention settings.
- Fix:
- Define retention by log type (app vs audit vs security)
- Sample verbose logs; reduce debug in prod
- Route to cheaper storage after N days
- Typical savings impact (typical): medium to high in observability-heavy stacks.
- Owner: DevOps + Security + App teams
11) Data egress charges (internet/outbound transfer)
- What it is: paying to move data out of cloud regions/providers.
- Why it happens: SaaS downloads, analytics exports, cross-cloud patterns, CDN misconfig.
- How to detect: egress reports; top talkers; spike analysis.
- Fix:
- Add CDN and caching where appropriate
- Keep compute close to data
- Reduce large outbound payloads; compress
- Typical savings impact (typical): medium; sometimes very high.
- Owner: Platform + App teams
12) Cross-AZ / cross-region traffic surprises
- What it is: “internal” traffic that is still billable across zones/regions.
- Why it happens: architecture spreads services; misconfigured load balancing; data replication.
- How to detect: network cost breakdown by AZ/region; service topology review.
- Fix:
- Co-locate chatty services
- Review multi-AZ patterns and necessity
- Reduce cross-region replication frequency
- Typical savings impact (typical): medium.
- Owner: Cloud architecture + App teams
13) Unbounded analytics exports and data duplication
- What it is: duplicate datasets stored multiple times, repeated ETL extracts.
- Why it happens: no governance; teams create their own pipelines.
- How to detect: storage growth; duplicate tables; repeated pipelines.
- Fix:
- Centralize canonical datasets
- Set data contracts and ownership
- Delete redundant datasets and enforce governance
- Typical savings impact (typical): medium.
- Owner: Data & Analytics + FinOps
C) Managed Services Misconfiguration (14–18)
14) Underutilized databases (oversized DB instances)
- What it is: DB instances with low CPU/IO, over-provisioned storage/throughput.
- Why it happens: “database fear,” overestimations, default provisioning.
- How to detect: CPU <10%, low IOPS, low connections; idle replicas.
- Fix:
- Rightsize DB instance class
- Reduce replicas where safe
- Optimize queries and indexes before scaling up
- Typical savings impact (typical): medium to high.
- Owner: App team + DBA/Platform
15) Managed cache overprovisioning (Redis/Memcached etc.)
- What it is: caches sized for peak but idle most of the time.
- Why it happens: “bigger is safer,” no cache hit ratio monitoring.
- How to detect: low memory utilization; low request rate; low hit ratio benefits.
- Fix:
- Tune cache size and eviction policies
- Remove unused caches
- Ensure cache actually reduces DB load
- Typical savings impact (typical): low to medium.
- Owner: App team + Platform
16) Managed message/stream services sized incorrectly
- What it is: throughput units, partitions, or brokers sized too high.
- Why it happens: cautious provisioning; no throughput forecasting.
- How to detect: low throughput utilization; low consumer lag needs.
- Fix:
- Right-size partitions/throughput
- Implement autoscaling where supported
- Set retention appropriately
- Typical savings impact (typical): medium in event-heavy systems.
- Owner: Platform + App teams
17) Serverless “death by a thousand cuts” (misconfigured)
- What it is: high invocations, long runtimes, over-allocated memory.
- Why it happens: no profiling; retries; chatty architecture.
- How to detect: cost by function; cold start frequency; duration/memory mismatch.
- Fix:
- Tune memory vs runtime
- Reduce invocations via batching
- Fix retries/timeouts and idempotency
- Typical savings impact (typical): low to medium; can be high in high-scale.
- Owner: App team
18) Default high availability settings everywhere
- What it is: paying for multi-region/extra replicas you don’t need.
- Why it happens: “checkbox architecture” without risk analysis.
- How to detect: cost vs SLA needs; redundancy config review.
- Fix:
- Align HA with real business SLA
- Use multi-AZ where required, not everywhere
- Document risk-based decisions
- Typical savings impact (typical): medium.
- Owner: Architecture + CFO/CTO alignment
D) Commitment & Pricing Strategy Mistakes (19–22)
19) Misused reserved instances / savings plans / commitments
- What it is: commitments bought that don’t match actual usage patterns.
- Why it happens: buying before understanding baseline; poor forecasting; no tracking.
- How to detect: low utilization/coverage; unused commitments.
- Fix:
- Establish baseline usage model
- Buy commitments gradually (phased)
- Track coverage/utilization weekly
- Typical savings impact (typical): medium to high if corrected.
- Owner: FinOps + Platform + Finance
20) No commitment strategy (everything on-demand)
- What it is: paying peak rates when workloads are stable.
- Why it happens: fear of lock-in; lack of forecasting.
- How to detect: high on-demand spend for steady workloads.
- Fix:
- Identify steady baseline workloads
- Commit only to the baseline; leave burst on-demand/spot
- Review monthly and adjust
- Typical savings impact (typical): medium.
- Owner: FinOps + Finance
21) Not using scheduling/spot for batch and CI workloads
- What it is: expensive on-demand compute for interruptible or time-flexible jobs.
- Why it happens: pipelines not designed for interruption; no queueing.
- How to detect: CI/CD cost spikes; batch workloads always on-demand.
- Fix:
- Use spot/preemptible where safe
- Add retries and checkpointing
- Schedule batch during cheaper windows if applicable
- Typical savings impact (typical): medium to high for compute-heavy teams.
- Owner: DevOps + Engineering
22) Paying for premium support tiers without a plan
- What it is: high support spend not tied to business impact.
- Why it happens: bought “just in case,” not reviewed.
- How to detect: support cost vs incident history and SLA needs.
- Fix:
- Align support tier to risk profile
- Review quarterly based on incidents and uptime needs
- Typical savings impact (typical): low to medium.
- Owner: CTO/CIO + Finance
E) Governance, Tagging, and Process Gaps (23–25)
23) Missing tagging / no chargeback or showback
- What it is: costs can’t be attributed to teams/products/environments.
- Why it happens: tagging isn’t enforced; no ownership culture.
- How to detect: tag coverage <80–90%; “unallocated spend” growing.
- Fix:
- Define required tags (Owner, Team, Product, Env, CostCenter)
- Enforce tag policies and block noncompliant resources (where feasible)
- Implement showback/chargeback reporting
- Typical savings impact (typical): indirect but major—drives behavior change.
- Owner: FinOps + Platform + Finance
24) No budgets, alerts, or anomaly detection
- What it is: bills surprise you at month end.
- Why it happens: “we’ll check later,” or nobody owns alerts.
- How to detect: no budget thresholds; no anomaly alerts configured.
- Fix:
- Set budgets per environment/team
- Configure anomaly alerts for spikes
- Route alerts to owners with action playbooks
- Typical savings impact (typical): medium via prevention.
- Owner: FinOps + Platform
25) Duplicate tools and observability spend
- What it is: paying for overlapping APM/logging/security tools.
- Why it happens: teams buy tools independently; no standard platform.
- How to detect: multiple vendors for similar telemetry; overlapping ingestion.
- Fix:
- Standardize observability stack
- Reduce log volume and duplicate ingestion
- Consolidate licenses/contracts
- Typical savings impact (typical): medium; sometimes high in tool-heavy orgs.
- Owner: CTO/CIO + FinOps + Security
Common Mistake: Running one-off “cost cutting weeks” without changing governance. You’ll save money once—and then drift back up.
Copy/Paste Tracker Format
Use these columns in a spreadsheet for each checklist item:
- Item #
- Waste area
- Category (A–E)
- Detection signal/source
- Current monthly cost (estimate)
- Fix action(s)
- Propriétaire
- Priority (P1/P2/P3)
- Effort (S/M/L)
- Risk (Low/Med/High)
- Start date / target date
- Savings achieved (monthly)
- Status (Not started / In progress / Done)
- Notes / evidence link
30-Day Cloud Cost Optimization Sprint Plan
This sprint plan is designed to deliver quick wins while building lasting governance.
30-day sprint plan table
| Week | Focus | Key deliverables | Owners |
| Week 1 | Visibility + tagging + quick kills | tag policy, top 10 waste list, budgets/alerts, kill zombies | FinOps + Platform |
| Week 2 | Rightsizing + scheduling + storage cleanup | rightsizing actions, non-prod schedules, snapshot/volume cleanup | Platform + App teams |
| Week 3 | Commitments + network fixes | commitment baseline, utilization tracking, egress/cross-AZ fixes | FinOps + Cloud arch |
| Week 4 | Governance + automation + KPI cadence | FinOps cadence, RACI, automation policies, monthly review process | CFO/FinOps + CTO |
Week 1: Visibility + tagging + quick kills
- Establish current baseline spend by account/env/service
- Enforce required tags for new resources (where feasible)
- Turn on budgets + anomaly alerts
- Identify and remove zombie resources
- Publish a weekly cost report (simple)
Week 2: Rightsizing + scheduling + storage cleanup
- Rightsize top 10 compute spend items
- Schedule dev/stage off-hours
- Clean orphaned volumes/snapshots
- Review log retention and sampling
- Validate performance and rollbacks
Week 3: Commitments + architecture/network fixes
- Analyze baseline usage for commitments
- Buy commitments in phases (baseline only)
- Reduce data egress and cross-AZ chatter
- Optimize NAT/LB usage patterns (where applicable)
- Fix the top 2 managed service misconfigs
Week 4: Governance + automation + KPI cadence
- Define FinOps KPIs and cadence
- Establish approval workflow for high-cost changes
- Automate cleanup policies (TTL, schedules)
- Implement showback/chargeback reporting
- Build monthly optimization backlog
FinOps Operating Model
Roles + cadence (minimum)
- Weekly: FinOps review (top drivers, anomalies, actions)
- Monthly: rightsizing + commitments review, showback report
- Quarterly: architecture review for structural cost improvements
FinOps KPIs (simple and powerful)
- Cost per environment (prod vs non-prod)
- Unallocated spend (% without tags)
- Commitment coverage and utilization
- Unit cost metric (e.g., cost per customer, per 1k requests, per job)
- Top 10 services by cost and trend
Simple RACI table
| Activity | CFO/Finance | FinOps Lead | Platform/Cloud | App Teams | Security |
| Budget targets + reporting | A | R | C | C | C |
| Tagging policy enforcement | C | R | A | C | C |
| Rightsizing + scheduling | C | C | A/R | R | C |
| Commitment planning | A | R | C | C | C |
| Tooling consolidation | A | R | C | C | A/C |
| Governance + approval gates | A | R | A | C | A/C |
(R=Responsible, A=Accountable, C=Consulted)
Common failure patterns (and fixes)
- Failure: FinOps is “finance-only.”
- Fix: Make platform and app teams co-owners; tie actions to sprint backlogs.
- Failure: Tagging is optional.
- Fix: Enforce policies + block noncompliant resources where feasible.
- Failure: Savings are found but not sustained.
- Fix: Weekly cadence + automation + showback.
Frequent Questions
How often should we run cloud cost optimization?
Weekly for visibility and anomalies, monthly for rightsizing/commitments, and quarterly for architecture-level changes.
What’s the fastest way to reduce cloud costs?
Turn off idle/non-prod, delete zombie resources, fix log retention, and rightsize top spend services first.
What’s the fastest way to reduce cloud costs?
Turn off idle/non-prod, delete zombie resources, fix log retention, and rightsize top spend services first.
What are typical cloud savings?
Typical ranges vary by maturity. Organizations without FinOps hygiene often see 10–30% (typical). Mature orgs may see 5–15% (typical) through continuous optimization.
Is rightsizing safe?
Yes when done with measurement, staged changes, and rollback plans. Start with non-prod, then low-risk prod services.
Are savings plans/reserved instances worth it?
Often yes for steady baseline workloads—if you track utilization and avoid overcommitting.
How do we reduce Kubernetes costs?
Improve visibility by namespace, schedule non-prod clusters, rightsize nodes, reduce over-requested resources, and fix autoscaler settings.
What KPIs should FinOps track?
Tag coverage/unallocated spend, commitment utilization, unit cost metrics, anomaly counts, and top service cost trends.
Conclusion
Cloud savings don’t come from one heroic cleanup. They come from visibility + ownership + automation: you identify the top waste areas, assign owners, implement repeatable guardrails, and review KPIs on a cadence. That’s how you reduce cloud spend without trading away reliability.
If you want a fast start, Gigabit can run a FinOps Quick Audit Call and deliver a 30-day optimization sprint plan tailored to your AWS/Azure/GCP footprint and operating model. “Gigabit fuses world-class design, scalable engineering and AI to build software solutions that power digital transformation.”
